A method for analyzing the fading characteristics of signal transmission in a cascade channel and related products

Abstract

The present invention discloses a method for analyzing the fading characteristics of signal transmission in a cascade channel and related products, and relates to the field of wireless communication technology. The method obtains the values ​​of the first parameter, the second parameter and the third parameter of each channel in the cascade channel; the first parameter represents the propagation characteristics of the signal under the influence of nonlinear effects; the second parameter represents the relative strength between the direct component and the scattered component in the corresponding channel; the third parameter represents the number of multipath clusters in the corresponding channel; the values ​​of the first parameter, the second parameter and the third parameter of each channel are input into a product distribution model, and the fading characteristics of the cascade channel are determined by the probability density function and the cumulative distribution function in the product distribution model; the product distribution model is used to characterize the fading behavior of a multipath cluster signal containing a direct path when passing through a cascade channel under the action of nonlinear effects. The method can simulate the fading behavior of a multipath cluster signal containing a direct path when passing through a cascade channel under nonlinear effects.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a fading characteristic analysis method for signal transmission in a cascade channel and related products. Background Art

[0002] In the field of wireless communications, signals are affected by many factors during propagation, such as multipath fading, shadow fading, and noise interference. The combined effect of these factors makes the signal's amplitude and phase characteristics appear random. Statistical distribution models can describe this random phenomenon in a probabilistic way, thereby providing a macroscopic description of signal propagation characteristics.

[0003] With the continuous development of communication technology, cascade channels are becoming more and more common in multiple complex communication scenarios. These complex communication scenarios usually operate in different propagation environments, and the received signal can be modeled as the product of multiple random envelopes. However, the existing product distribution models have some limitations. For example, many existing product distribution models only consider the product of two distributions when they are constructed, which makes it difficult for them to deal with multiple cascade links or multipath components that may be involved in actual communication scenarios. In addition, the existing product distribution models cannot be effectively applied to complex channel conditions due to their structure and parameter settings. For example, the double Nakagami-m product distribution is not suitable for channel environments where strong direct paths and severe multipath scattering exist at the same time and the multipath components have different fading characteristics; the double κ-μ product distribution is not suitable for describing the propagation characteristics of nonlinear signals.

[0004] Therefore, the product distribution model in the prior art cannot effectively describe the fading behavior of a multipath cluster signal containing a direct path when passing through a cascade channel under nonlinear effects. Summary of the invention

[0005] Based on this, it is necessary to provide a fading characteristic analysis method and related products for signal transmission in a cascade channel to address the above technical problems. This method can simulate the fading behavior of a multipath cluster signal containing a direct path when passing through a cascade channel under nonlinear effects.

[0006] The present invention adopts the following technical solutions:

[0007] The present invention provides a method for analyzing fading characteristics of signal transmission in a cascade channel, comprising:

[0008] Obtain the values ​​of the first parameter, the second parameter and the third parameter of each channel in the cascaded channel; the first parameter represents the propagation characteristics of the signal under the influence of nonlinear effects; the second parameter represents the relative strength between the direct component and the scattered component in the corresponding channel; the third parameter represents the number of multipath clusters in the corresponding channel;

[0009] The values ​​of the first parameter, the second parameter and the third parameter of each channel are input into the product distribution model. The values ​​of the first parameter, the second parameter and the third parameter of each channel are analyzed through the probability density function and the cumulative distribution function in the product distribution model to obtain the fading characteristics of the cascade channel; the product distribution model is used to characterize the fading behavior of multipath cluster signals containing direct paths when passing through the cascade channel under the action of nonlinear effects.

[0010] Preferably, the fading characteristics of the cascaded channels are obtained by analyzing the values ​​of the first parameter, the second parameter, and the third parameter of each channel by multiplying the probability density function and the cumulative distribution function in the distribution model, including:

[0011] Inputting the values ​​of the first parameter, the second parameter and the third parameter of each channel into the probability density function, obtaining the probability distribution of the received signal strength of the cascade channel under different fading conditions;

[0012] Inputting the values ​​of the first parameter, the second parameter and the third parameter of each channel into a cumulative distribution function to obtain a probability distribution of the received signal strength of the cascaded channel at a preset signal strength level;

[0013] The fading characteristics of the cascaded channels are determined according to the probability distribution of the received signal strength of the cascaded channels under different fading conditions and the probability distribution of the received signal strength at a preset signal strength level.

[0014] Preferably, the expression of the probability density function is:

[0015]

[0016] The expression of the cumulative distribution function is:

[0017]

[0018] in, is the Meijer-G function,

[0019] Γ(·) is the gamma function, 1F1(·; ·; ·) is the confluent hypergeometric function, m is the number of channels, α is the first parameter, κ is the second parameter, and μ is the third parameter.

[0020] Preferably, the product distribution model further includes a high-order origin moment function, an outage probability function, an average bit error rate function and an ergodic capacity function; and the method further includes:

[0021] Inputting the values ​​of the first parameter, the second parameter and the third parameter of each channel into the high-order origin moment function, the outage probability function, the average bit error rate function and the ergodic capacity function to determine the fading characteristics of the cascaded channel;

[0022] The expression of the higher-order origin moment function is:

[0023]

[0024] The expression of the outage probability function is:

[0025]

[0026] The expression of the average bit error rate function is:

[0027]

[0028] The expression of the traversal capacity function is:

[0029]

[0030] in, is the Fox-H function, δ1 and δ2 are the modulation coefficients.

[0031] Preferably, the product distribution model is compatible with cascade operations between multiple distribution models, including Nakagami-m, one-sided Gaussian, Weibull, κ-μ, α-μ, Rayleigh and Rice distributions.

[0032] The present invention provides a fading characteristic analysis device for signal transmission in a cascade channel, the fading characteristic analysis device for signal transmission in a cascade channel comprising:

[0033] An acquisition module is used to acquire a first parameter, a second parameter and a third parameter of each channel in the cascaded channel; the first parameter represents the propagation characteristics of the signal under the influence of nonlinear effects; the second parameter represents the relative strength between the direct component and the scattered component in the corresponding channel; the third parameter represents the number of multipath clusters in the corresponding channel;

[0034] The simulation module is used to input the first parameter, the second parameter and the third parameter of each channel into the product distribution model, and analyze the values ​​of the first parameter, the second parameter and the third parameter of each channel through the probability density function and the cumulative distribution function in the product distribution model to obtain the fading characteristics of the cascade channel; the product distribution model is used to characterize the fading behavior of the multipath cluster signal containing the direct path when passing through the cascade channel under the action of nonlinear effects.

[0035] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the method for analyzing fading characteristics of signal transmission in the cascade channel is implemented.

[0036] The present invention provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned fading characteristic analysis method of signal transmission in a cascade channel when executing the program.

[0037] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects:

[0038] In the present invention, since the first parameter represents the propagation characteristics of the signal under the influence of nonlinear effects; the second parameter represents the relative intensity between the direct component and the scattered component in the corresponding channel; the third parameter represents the number of multipath clusters in the corresponding channel, and the product distribution model is used to characterize the fading behavior of the multipath cluster signal with a direct path when passing through the cascade channel under the nonlinear effect, therefore, the probability density function and the cumulative density function in the product distribution model are combined with the values ​​of the first parameter, the second parameter and the third parameter of each channel, so as to simulate the fading behavior of the multipath cluster signal with a direct path when passing through the cascade channel under the nonlinear effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0040] Figure 1 A schematic flow chart of a method for analyzing fading characteristics of signal transmission in a cascade channel provided by the present invention;

[0041] Figure 2 It is a flow chart for the derivation of the distribution of multiple α-κ-μ products;

[0042] Figure 3 The PDF function of the α-κ-μ product distribution changes with the parameters α, κ and μ when m = 2;

[0043] Figure 4 The PDF function of the α-κ-μ product distribution changes with the parameters α, κ and μ when m=3;

[0044] Figure 5 The CDF function of the α-κ-μ product distribution changes with the parameters α, κ and μ when m = 2;

[0045] Figure 6 The CDF function of the α-κ-μ product distribution changes with the parameters α, κ and μ when m=3;

[0046] Figure 7 A schematic diagram of a fading characteristic analysis device for signal transmission in a cascade channel provided by the present invention;

[0047] Figure 8 A schematic diagram of a computer device for implementing a method for analyzing fading characteristics of signal transmission in a cascade channel provided by the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0049] With the increasing maturity of 5G and 6G technologies, cascade communications are becoming more and more common, such as multi-hop systems, cascade channels, multi-input multi-output systems (MIMO) and radar clutter detection systems. These complex communication scenarios usually operate under different propagation conditions, and the received signal can be modeled as the product of multiple random envelopes. To this end, the present invention proposes a fading characteristic analysis method for signal transmission in a cascade channel and related products, which can simulate the fading behavior of a multipath cluster signal containing a direct path when passing through a cascade channel under nonlinear effects.

[0050] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0051] Figure 1 The present invention is a flowchart of a method for analyzing the fading characteristics of signal transmission in a cascade channel, which specifically includes the following steps:

[0052] S101, obtaining the values ​​of the first parameter, the second parameter and the third parameter of each channel in the cascade channel; the first parameter represents the propagation characteristics of the signal under the influence of nonlinear effects; the second parameter represents the relative intensity between the direct component and the scattered component in the corresponding channel; the third parameter represents the number of multipath clusters in the corresponding channel.

[0053] The values ​​of the first parameter, the second parameter and the third parameter of each channel in the cascaded channel may be determined according to the signal transmission condition of each channel in the cascaded channel.

[0054] The values ​​of the first parameter, the second parameter and the third parameter may also be determined according to requirements to determine the fading of the cascade channel under the corresponding channel conditions.

[0055] It should be noted that the first parameter is the nonlinear coefficient, which refers to the propagation characteristics of the signal under the influence of nonlinear effects (including nonlinear refraction effects in the ionosphere, nonlinear scattering phenomena in plasma, nonlinear fading in atmospheric turbulence, and nonlinear reflection on the ocean surface); a larger first parameter (the value of the first parameter > 0) indicates that the nonlinear effect is stronger, a larger second parameter (the value of the second parameter > 0) indicates that the direct component is stronger than the scattered component, and a larger third parameter (the value of the third parameter > 0) represents a greater number of multipath clusters.

[0056] S102, inputting the values ​​of the first parameter, the second parameter and the third parameter of each channel into the product distribution model, analyzing the values ​​of the first parameter, the second parameter and the third parameter of each channel through the probability density function and the cumulative distribution function in the product distribution model, and obtaining the fading characteristics of the cascade channel; the product distribution model is used to characterize the fading behavior of a multipath cluster signal containing a direct path when passing through a cascade channel under the action of nonlinear effects.

[0057] The product distribution model may be a multi-α-κ-μ product distribution model, where the first parameter is α, the second parameter is κ, and the third parameter is μ. The multi-α-κ-μ product distribution model is used to characterize the fading behavior of a multipath cluster signal containing a direct path when passing through a cascade channel under the action of nonlinear effects.

[0058] The present invention provides a multi-α-κ-μ product distribution model including expression of probability density function (PDF), cumulative distribution function (CDF), high-order origin moment function, outage probability (OP) function, average bit error rate (ABER) function and ergodic capacity (EC) function based on signal-to-noise ratio. Figure 2 As shown, Figure 2 This is a flow chart for the derivation of multiple α-κ-μ product distributions; these expressions are all related to the α-κ-μ signal-to-noise ratio PDF, so it is necessary to first derive the PDF of the α-κ-μ distribution based on the signal-to-noise ratio.

[0059] The α-κ-μ fading distribution is based on the following assumption: Consider that the signal will generate μ multipath clusters during transmission, and the scattered waves on each cluster have the same power σ 2 At this time, if the received signal has a nonlinear envelope Where α is the nonlinear coefficient, p i and q i They are the in-phase components X i and the orthogonal component Yi The mean of To quantify the relative intensity between the direct component and the scattered component, where d 2 Represents the total power of the direct component. The normalized envelope of the α-κ-μ distribution can be obtained The PDF is as follows:

[0060]

[0061] Among them I v (·) denotes the ν-order modified Bessel function of the first kind.

[0062] SNR The average signal-to-noise ratio Combining formula (1), we can get the PDF of α-κ-μ distribution based on signal-to-noise ratio as follows:

[0063]

[0064] Among them, the parameters Γ(·) is the gamma function, and 1F1(·; ·; ·) is the confluent hypergeometric function.

[0065] After obtaining the α-κ-μ signal-to-noise ratio PDF, we assume that the number of products is m and let X = γ1γ2…γ m , where each γ i (i=1…m) satisfies the α-κ-μ distribution, that is, formula (2). Each α-κ-μ distribution has its own parameter α i , κ i and μ i (i=1…m). According to the iterative formula The PDF expression of the multi-α-κ-μ product distribution model can be obtained as follows:

[0066]

[0067] in, is the Meijer-G function with parameters

[0068] b=(b( 1 ),…,b( m )), m is the number of channels, α is the first parameter, κ is the second parameter, and μ is the third parameter.

[0069] According to the PDF expression of the proposed multi-α-κ-μ product distribution model, its CDF expression is further derived as follows:

[0070]

[0071] Based on the α-κ-μ signal-to-noise ratio PDF expression (2), the h-order moment expression of the signal-to-noise ratio γ can be obtained as follows:

[0072]

[0073] according to The product X = γ1γ2…γ m The expression of the h-order origin moment is as follows:

[0074]

[0075] Among them, the parameters

[0076] Given a threshold γ th According to formula (4), the interruption probability expression of the α-κ-μ product distribution is as follows:

[0077]

[0078] According to the expression The average bit error rate of the α-κ-μ product distribution is as follows:

[0079]

[0080] in, is the Fox-H function, δ1 and δ2 are the modulation coefficients.

[0081] Different modulation methods can be adopted according to the values ​​of δ1 and δ2. The specific values ​​are as follows:

[0082]

[0083] According to the expression It can be seen that the expression of the ergodic capacity of the α-κ-μ product distribution is as follows:

[0084]

[0085] By plotting the PDF and CDF simulation graphs based on the signal-to-noise ratio and observing the changing trends of the simulation curves in terms of peak degree and skewness, the physical meaning behind them can be revealed. This analysis helps to deeply understand the fading characteristics of multiple α-κ-μ product distributions under different transmission conditions, and provides important guidance for parameter optimization and the formulation of effective cascade communication strategies.

[0086] Therefore, in the case where the product distribution model includes a probability density function and a cumulative distribution function, the values ​​of the first parameter, the second parameter and the third parameter of each channel are analyzed by the probability density function and the cumulative distribution function in the product distribution model to obtain the fading characteristics of the cascaded channel, including: inputting the values ​​of the first parameter, the second parameter and the third parameter of each channel into the probability density function to obtain the probability distribution of the received signal strength of the cascaded channel under different fading conditions; inputting the values ​​of the first parameter, the second parameter and the third parameter of each channel into the cumulative distribution function to obtain the probability distribution of the received signal strength of the cascaded channel at a preset signal strength level; determining the fading characteristics of the cascaded channel according to the probability distribution of the received signal strength of the cascaded channel under different fading conditions and the probability distribution of the received signal strength at a preset signal strength level. The probability distribution of the received signal strength of the cascade channel under different fading conditions and the probability distribution of the received signal strength at a preset signal strength level can be directly determined as the fading characteristics of the cascade channel; the probability distribution of the received signal strength of the cascade channel under different fading conditions and the probability distribution of the received signal strength at a preset signal strength level can also be analyzed, and the analysis results can be determined as the fading characteristics of the cascade channel.

[0087] Optionally, the product distribution model also includes a high-order origin moment function, an outage probability function, an average bit error rate function and an ergodic capacity function; and the statistical characteristics of the high-order origin moment, outage probability, average bit error rate and ergodic capacity can not only be widely used in fields such as parameter estimation and error analysis, but also have important value in communications, signal processing and statistical modeling.

[0088] Therefore, this embodiment may also include: inputting the values ​​of the first parameter, the second parameter and the third parameter of each channel into the high-order origin moment function, the interruption probability function, the average bit error rate function and the ergodic capacity function to determine the fading characteristics of the cascaded channel, that is, the fading characteristics of the cascaded channel may also include the high-order origin moment, interruption probability, average bit error rate and ergodic capacity of the cascaded channel.

[0089] Therefore, the fading characteristics of the cascaded channel may include the probability distribution of the received signal strength of the cascaded channel under different fading conditions, as well as the probability distribution of the received signal strength at a preset signal strength level, high-order origin moment, outage probability, average bit error rate and ergodic capacity.

[0090] In an exemplary embodiment, the product distribution model is compatible with cascading operations between multiple distribution models, including Nakagami-m, one-sided Gaussian, Weibull, κ-μ, α-μ, Rayleigh, and Rice distributions.

[0091] The multiple α-κ-μ product distribution models of the present invention can adapt to any number of cascade channels by flexibly adjusting their parameters, and are compatible with a variety of product distributions, such as models obtained by multiplying any number of Nakagami-m, Rice, Rayleigh, unilateral Gaussian, α-μ, κ-μ, Weibull and other distributions, have a higher degree of freedom, and can better characterize the small-scale fading characteristics of the signal when passing through the cascade channel.

[0092] In an exemplary embodiment, the α-κ-μ distribution used in the present invention has great parameter flexibility, which can be degenerated into Nakagami-m, Rayleigh, Ricean, one-sided Gaussian, α-μ, κ-μ and Weibull distributions. The specific parameter values ​​are shown in Table 1.

[0093] Table 1

[0094]

[0095] The present invention also draws PDF and CDF simulation diagrams of multiple α-κ-μ product distributions based on signal-to-noise ratio, and deeply analyzes the average signal-to-noise ratio. The changing rules of PDF and CDF curves under the following conditions:

[0096] like Figure 3-Figure 6 As shown, Figure 3 The PDF function of the α-κ-μ product distribution changes with the parameters α, κ and μ when m = 2; Figure 4 The PDF function of the α-κ-μ product distribution changes with the parameters α, κ and μ when m=3; Figure 5 The CDF function of the α-κ-μ product distribution changes with the parameters α, κ and μ when m = 2; Figure 6 The CDF function of the α-κ-μ product distribution when m=3 changes with the parameters α, κ and μ.

[0097] The Monte Carlo simulation shows a good fit with the curve, which confirms the accuracy of the proposed model. Figure 3 and Figure 4 The PDF curve in becomes increasingly thinner and taller. The reason is that according to the definition As κ and μ increase, the location parameter d 2 will increase or scale parameter σ 2Decreases. According to the knowledge related to normal distribution, in this case the curve will move to the right and become more "thin and tall". In addition, a larger κ means that the influence of the direct path signal gradually increases and dominates, which in turn causes the signal envelope to be more concentrated near the signal mean (direct path envelope), causing the curve to become more "thin and tall". A larger μ means that the number of multipath clusters increases, and more multipath components will be superimposed on each other, making the interference and fluctuations in the signal more uniform, so the curve shape is closer to a normal distribution. As the nonlinear coefficient α increases, the nonlinear effects of the in-direction component and the orthogonal component are enhanced. At this time, the signal power received by the receiver will increase and be more concentrated in certain directions. The greater the probability of the curve, the more stable the signal, and an obvious single peak phenomenon will appear.

[0098] Figure 5 and Figure 6 It is shown that the distribution of multiple α-κ-μ products corresponds to Figure 3 and Figure 4 From the CDF graph, it is obvious that the CDF curve tends to 1. As can be seen from the figure, when the parameters α, μ and κ are smaller, the CDF curve is higher, which means that the probability of the signal reaching or falling below a certain value is greater, and the signal fading phenomenon is more significant. In addition, under low signal-to-noise ratio conditions, it can be clearly observed that the CDF curve shows several orders of magnitude differences with the changes in parameters α, μ and κ. This feature gives the multi-α-κ-μ product distribution extremely high flexibility, enabling it to optimize parameters through this flexibility to better adapt to real communication scenarios. For example, the required fading margin can be determined by referring to the relationship between the allowed outage probability (vertical axis) and the signal-to-noise ratio in the CDF graph. Taking the dual-channel scenario (α=(2.0,2.0), κ=(3.5,3.0) and μ=(1.3,1.5)) as an example, if the system needs to achieve an outage probability of less than 1%, a fading margin of 13dB (relative to the median) needs to be set. Figure 5 and Figure 6 As shown in Figure 2, smaller α, μ, and κ are associated with higher signal interruption probability, that is, larger fading amplitude, so a larger fading margin needs to be designed. Figure 5 and Figure 6 It can be found that as the number of products increases, the stability of the signal decreases and the probability of system interruption increases.

[0099] The present invention proposes a multi-α-κ-μ product distribution model, which introduces parameters κ, μ and α to more accurately characterize the fading characteristics of multipath cluster signals with multiple direct paths under the influence of nonlinear effects when passing through the cascade channel in the cascade communication scenario. The present invention provides a probability density function of the product distribution model based on the signal-to-noise ratio, and its statistical characteristics: including cumulative distribution function, high-order origin moment, interruption probability, average bit error rate and ergodic capacity. By analyzing the PDF and CDF of the multi-α-κ-μ product distribution, the present invention reveals the influence mechanism of parameters κ, μ, α and the number of products on the signal fading characteristics: a larger κ will make the signal envelope more concentrated on the direct component; smaller μ and α will increase the probability of signal interruption; a larger number of products will make the signal fade more severely. The fading characteristics of the signal under different transmission conditions are systematically quantified, providing a solid theoretical basis for optimizing the performance of the communication system and improving the transmission efficiency. In addition, the multi-α-κ-μ product distribution model can be compatible with cascade operations including any number of Nakagami-m, one-sided Gaussian, Weibull, κ-μ, α-μ, Rayleigh and Rice distribution models by adjusting parameters, so it has extremely high flexibility.

[0100] In summary, the multi-α-κ-μ product distribution model proposed in the present invention not only helps to accurately characterize the fading characteristics of signals in complex environments and is more in line with actual cascade communication scenarios, but also lays a theoretical foundation for designing more robust and efficient wireless cascade communication systems.

[0101] Therefore, the multi-α-κ-μ product distribution model of the cascaded channel in the present invention overcomes the limitations of the existing product distribution model in describing the signal propagation characteristics, thereby more accurately describing the propagation characteristics of the signal in a complex channel environment, and providing effective theoretical model support for the design, optimization and performance evaluation of the communication system.

[0102] Moreover, the multi-α-κ-μ product distribution model is not only highly flexible in parameter selection, but also suitable for complex cascade communication scenarios, such as multi-hop, multi-relay, cascade channels, MIMO systems, radar clutter detection, etc.; therefore, it shows a broader application prospect and can provide a solid theoretical basis for improving the reliability and performance optimization of communication systems.

[0103] When applying the fading characteristic analysis method of signal transmission in the cascade channel provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0104] The above is a method for analyzing the fading characteristics of signal transmission in a cascade channel provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding device for analyzing the fading characteristics of signal transmission in a cascade channel, such as Figure 7 shown.

[0105] Figure 7 A schematic diagram of a fading characteristic analysis device for signal transmission in a cascade channel provided by the present invention, the device 700 includes:

[0106] The acquisition module 701 is used to acquire the first parameter, the second parameter and the third parameter of each channel in the cascaded channel; the first parameter represents the propagation characteristics of the signal under the influence of nonlinear effects; the second parameter represents the relative strength between the direct component and the scattered component in the corresponding channel; the third parameter represents the number of multipath clusters in the corresponding channel;

[0107] The simulation module 702 is used to input the first parameter, the second parameter and the third parameter of each channel into the product distribution model, and analyze the values ​​of the first parameter, the second parameter and the third parameter of each channel through the probability density function and the cumulative distribution function in the product distribution model to obtain the fading characteristics of the cascade channel; the product distribution model is used to characterize the fading behavior of a multipath cluster signal containing a direct path when passing through a cascade channel under the action of nonlinear effects.

[0108] For the specific definition of the fading characteristic analysis device for signal transmission in cascade channels, please refer to the definition of the fading characteristic analysis method for signal transmission in cascade channels mentioned above, which will not be repeated here. Each module in the above-mentioned fading characteristic analysis device for signal transmission in cascade channels can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0109] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A method for analyzing the fading characteristics of signal transmission in a cascade channel is provided.

[0110] The present invention also provides Figure 8 The structural diagram of the computer device shown in FIG. Figure 8 As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 A method for analyzing the fading characteristics of signal transmission in a cascade channel is provided.

[0111] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0112] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A method for analyzing fading characteristics of signal transmission in a cascade channel, characterized in that: The method comprises: Obtaining the values ​​of the first parameter, the second parameter and the third parameter of each channel in the cascaded channel; the first parameter represents the propagation characteristics of the signal under the influence of nonlinear effects; the second parameter represents the relative strength between the direct component and the scattered component in the corresponding channel; the third parameter represents the number of multipath clusters in the corresponding channel; The values ​​of the first parameter, the second parameter and the third parameter of each channel are input into a product distribution model, and the values ​​of the first parameter, the second parameter and the third parameter of each channel are analyzed through the probability density function, the cumulative distribution function, the higher-order origin moment function, the interruption probability function, the average bit error rate function and the ergodic capacity function in the product distribution model to obtain the fading characteristics of the cascade channel; the product distribution model is used to characterize the fading behavior of a multipath cluster signal containing a direct path when passing through a cascade channel under the action of nonlinear effects.

2. The method according to claim 1, characterized in that The method further comprises: Inputting the values ​​of the first parameter, the second parameter and the third parameter of each of the channels into the probability density function to obtain the probability distribution of the received signal strength of the cascade channel under different fading conditions; Inputting the values ​​of the first parameter, the second parameter and the third parameter of each of the channels into the cumulative distribution function to obtain the probability distribution of the received signal strength of the cascaded channel at a preset signal strength level; The fading characteristics of the cascade channel are determined according to the probability distribution of the received signal strength of the cascade channel under different fading conditions and the probability distribution of the received signal strength at a preset signal strength level.

3. The method according to claim 2, characterized in that The expression of the probability density function is: The expression of the cumulative distribution function is: in, is the Meijer-G function, Γ(·) is the gamma function, 1F1(·; ·; ·) is the confluent hypergeometric function, m is the number of channels, α is the first parameter, κ is the second parameter, and μ is the third parameter.

4. The method according to claim 3, characterized in that The expression of the high-order origin moment function is: The expression of the interruption probability function is: The expression of the average bit error rate function is: The expression of the ergodic capacity function is: in, is the Fox-H function, δ1 and δ2 are the modulation coefficients.

5. The method according to claim 1, characterized in that The product distribution model is compatible with cascade operations between multiple distribution models, including Nakagami-m, one-sided Gaussian, Weibull, κ-μ, α-μ, Rayleigh and Rice distributions.

6. A device for analyzing fading characteristics of signal transmission in a cascade channel, characterized in that: The device comprises: An acquisition module, used to acquire a first parameter, a second parameter and a third parameter of each channel in the cascaded channel; the first parameter represents the propagation characteristics of the signal under the influence of nonlinear effects; the second parameter represents the relative strength between the direct component and the scattered component in the corresponding channel; the third parameter represents the number of multipath clusters in the corresponding channel; A simulation module is used to input the first parameter, the second parameter and the third parameter of each channel into a product distribution model, and analyze the values ​​of the first parameter, the second parameter and the third parameter of each channel through the probability density function, the cumulative distribution function, the high-order origin moment function, the interruption probability function, the average bit error rate function and the ergodic capacity function in the product distribution model to obtain the fading characteristics of the cascade channel; the product distribution model is used to characterize the fading behavior of a multipath cluster signal containing a direct path when passing through a cascade channel under the action of nonlinear effects.

7. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

8. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 5 is implemented.

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